WH2014 Session: Know your audience predictors of success for a patient centered

KNOW YOUR AUDIENCE PREDICTORS OF

SUCCESS FOR A PATIENT-CENTERED

MARK J. SIEDNER

MASSACHUSETTS GENERAL HOSPITAL CENTER

FOR GLOBAL HEALTH

Know  Your  Audience:  Predictors  of  success  for  a  pa5ent-­‐

centered  SMS  applica5on  to  augment  HIV  linkage  to  care  in  rural  Uganda  

Research  Abstract,  Wireless  Health  2014  Oct  29-­‐31,  2014,  Bethesda,  MD,  USA  

Mark  J.  Siedner  Massachuse1s  General  Hospital  Center  for  Global  Health  

Conflicts  of  Interest  and  Funding  

•  No  financial  conflicts  of  interest  to  report  •  I  receive  salary  and  research  support  from:  – Fogarty  InternaEonal  Center  R24  TW  007988  

– NaEonal  InsEtutes  of  Health  K23  MH  099916  – Harvard  Center  for  AIDS  Research  

Great  PotenEal  for  Mobile  Health  in  sub-­‐Saharan  Africa  

InternaEonal  TelecommunicaEons  Union,  2014  

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2005 2006 2007 2008 2009 2010 2011 2011 2013 2014*

Fixed-telephone subscriptions

Mobile-cellular subscriptions

Individuals using the Internet

MILL

IONS

Have  we  harnessed  that  potenEal?  

Keys  to  Successful  mHealth  ImplementaEon  

 Locally  defined  and  prioriEzed  health  problem    Conceptual  framework  – How  (and  if)  mobile  health  can  address  the  problem  

–  Is  it  sufficient  to  do  so?  

 Co-­‐creaEon  (or  local  creaEon)     Comprehensive  evaluaEon  by  the  end-­‐user  acceptability,  feasibility  and  acceptance  

 IteraEve  assessment  (develop    pilot    efficacy    effecEveness    implementaEon)  

2)  Conceptual  Framework  

ART  Eligibility   ART  Ini5a5on  

ART  Eligibility   ART  Ini5a5on  

Lack  of  Awareness  of  ART  Eligibility  (CommunicaEon)  

2)  Conceptual  Framework  

2)  Conceptual  Framework  

ART  Eligibility   ART  Ini5a5on  

Poverty  (TransportaEon  Costs)  

Lack  of  Awareness  of  ART  Eligibility  (CommunicaEon)  

2)  Conceptual  Framework  

ART  Eligibility   ART  Ini5a5on  

Poverty  (TransportaEon  Costs)  

Lack  of  Awareness  of  ART  Eligibility  (CommunicaEon)  

1.  SMS  No5fica5on  of  Laboratory  Results  

2)  Conceptual  Framework  

ART  Eligibility   ART  Ini5a5on  

Poverty  (TransportaEon  Costs)  

Lack  of  Awareness  of  ART  Eligibility  (CommunicaEon)  

1.  SMS  No5fica5on  of  Laboratory  Results  

2.  Transporta5on  Reimbursement  

3)  Co-­‐Create  

•  Development  team  – Clinic  staff  – Clinic  database  management  team  – Research  team  

•  Biomedical  

•  Anthropology  •  Social  scienEsts  

– Dimagi  Inc  (web  development  company)  – Yo!  Uganda  (content  aggregator)  

4)  End-­‐User  Acceptability  

•  Pre-­‐study  survey  of  50  clinic  clients  – 100%  expressed  interest  in  a  cell  phone-­‐based  system  of  communicaEng  clinic  informaEon  

4)  End-­‐User  Acceptability  

 “It  will  save  the  cost  of  transport  because  we  come  and  we  find  nothing  ready  for  almost  four  :mes  of  coming  to  the  clinic  and  going  with  no  achievement.  So  it  would  make  us  come  when  we  are  sure.”  

5)  IteraEve  Assessment  

A.  Development  stage  – Dimagi  Design  

B.  Pilot  – Clinic  and  research  staff  pilot  tesEng  – Feedback  to  Dimagi  

C.  Efficacy  Trial  

Efficacy  Clinical  Trial  •  Eligibility  – Adults  in  HIV  care  in  Mbarara  

– Undergoing  “high-­‐risk”  CD4  count  tesEng  – Access  to  a  cell  phone  

Efficacy  Clinical  Trial  •  Study  groups  – Pre-­‐intervenEon  period  (standard  of  care)  –  IntervenEon  period  (SMS)  

•  Normal  laboratory  result:  single  SMS  

•  Abnormal  laboratory  result  –  RandomizaEon  

»  Direct  Message  

»  PIN-­‐protected  Message  

»  Coded  Message  (ABCDEFG)  –  Up  to  7  daily  messages  

–  TransportaEon  reimbursement  (~$6USD)  

Efficacy  Clinical  Trial  

Enrollment  

• Baseline  Survey  • CD4  TesEng  

CD4  Count  Result   Outcomes  Assessment  

• Group  DeterminaEon  • SMS  Scheduling  

• Follow-­‐up  Survey  

Efficacy  Clinical  Trial  

Efficacy  Trial  •  Message  DisseminaEon  

Efficacy  Trial  

DAYS  TO  CLINIC  RETURN  

Day  14  

DAYS  TO  ART  INITIATION  

Why  did  (and  didn’t)  it  work?  

•  Secondary  analysis  •  Predictors  of  successful  response  to  an  SMS-­‐based  intervenEon  for  paEent  end-­‐users  in  rural  Uganda  

Predictors  of  Response  

PIN  SMS  Message  +  Incen5ve:  

49  Abnormal  Results  

Coded  SMS  Message  +  Incen5ve:  

43  Abnormal  Results  

138  Abnormal  Results  

Direct  SMS  Message  +  Incen5ve:  

46  Abnormal  Results  

Randomiza5on  

Outcomes  of  Interest  

•  Self-­‐reported  receipt  of  at  least  one  SMS  •  Accurate  idenEficaEon  of  SMS  

•  Appropriate  clinic  return  – Abnormal  result:    ≤7  days  of  first  SMS  

Cohort  CharacterisEcs  

•  Median  age  30  •  55%  female  

•  Median  HH  income  $100/month  

•  60%  primary  educaEon  or  less  

•  72%  successful  read  a  sentence  on  enrollment  

Reported SMS Receipt Accurate SMS Identification

Return to Clinic <7 Days

AOR (95%CI) P-value AOR (95%CI) P-

value AOR (95%CI) P-value

Reported SMS Receipt Accurate SMS Identification

Return to Clinic <7 Days

AOR (95%CI) P-value AOR (95%CI) P-

value AOR (95%CI) P-value

Age <26 REF REF REF 26-32 0.97 (0.30 – 3.13) 0.97 0.33 (0.07 – 1.60) 0.17 0.71 (0.24 – 2.07) 0.53 33-39 1.43 (0.40 – 5.16) 0.59 0.98 (0.18 – 5.27) 0.99 0.72 (0.23 – 2.19) 0.56 ≥40 1.49 (0.41 – 5.45) 0.54 0.24 (0.05 – 1.19) 0.08 0.66 (0.22 – 1.95) 0.45 Female gender 0.95 (0.38 – 2.37) 0.92 1.30 (0.44 – 3.83) 0.63 1.15 (0.52 – 2.52) 0.73 CD4 Result ≤100 REF REF REF 101-350 1.08 (0.39 – 2.96) 0.89 0.51 (0.13 – 1.96) 0.33 0.28 (0.11 – 0.75) 0.011 Read a complete sentence

2.14 (0.85 – 5.39) 0.11 4.54 (1.42 – 14.47) 0.011* 3.81 (1.61 – 9.03) 0.002*

Accessed sample SMS on enrollment†

3.05 (0.76 – 12.21) 0.12 0.63 (0.08 – 4.68) 0.65 4.90 (1.06 – 22.61)

0.04*

Randomized SMS Format Direct REF REF REF PIN 0.76 (0.27 – 2.17) 0.61 0.11 (0.03 – 0.44) 0.002* 0.26 (0.10 – 0.66) 0.005* Coded 1.00 (0.31 – 3.20) 0.99 0.38 (0.08 – 1.80) 0.22 0.58 (0.22 – 1.55) 0.28

Reported SMS Receipt Accurate SMS Identification

Return to Clinic <7 Days

AOR (95%CI) P-value AOR (95%CI) P-

value AOR (95%CI) P-value

Age <26 REF REF REF 26-32 0.97 (0.30 – 3.13) 0.97 0.33 (0.07 – 1.60) 0.17 0.71 (0.24 – 2.07) 0.53 33-39 1.43 (0.40 – 5.16) 0.59 0.98 (0.18 – 5.27) 0.99 0.72 (0.23 – 2.19) 0.56 ≥40 1.49 (0.41 – 5.45) 0.54 0.24 (0.05 – 1.19) 0.08 0.66 (0.22 – 1.95) 0.45 Female gender 0.95 (0.38 – 2.37) 0.92 1.30 (0.44 – 3.83) 0.63 1.15 (0.52 – 2.52) 0.73 CD4 Result ≤100 REF REF REF 101-350 1.08 (0.39 – 2.96) 0.89 0.51 (0.13 – 1.96) 0.33 0.28 (0.11 – 0.75) 0.011 Read a complete sentence

2.14 (0.85 – 5.39) 0.11 4.54 (1.42 – 14.47) 0.011* 3.81 (1.61 – 9.03) 0.002*

Accessed sample SMS on enrollment†

3.05 (0.76 – 12.21) 0.12 0.63 (0.08 – 4.68) 0.65 4.90 (1.06 – 22.61)

0.04*

Randomized SMS Format Direct REF REF REF PIN 0.76 (0.27 – 2.17) 0.61 0.11 (0.03 – 0.44) 0.002* 0.26 (0.10 – 0.66) 0.005* Coded 1.00 (0.31 – 3.20) 0.99 0.38 (0.08 – 1.80) 0.22 0.58 (0.22 – 1.55) 0.28

Reported SMS Receipt Accurate SMS Identification

Return to Clinic <7 Days

AOR (95%CI) P-value AOR (95%CI) P-

value AOR (95%CI) P-value

Age <26 REF REF REF 26-32 0.97 (0.30 – 3.13) 0.97 0.33 (0.07 – 1.60) 0.17 0.71 (0.24 – 2.07) 0.53 33-39 1.43 (0.40 – 5.16) 0.59 0.98 (0.18 – 5.27) 0.99 0.72 (0.23 – 2.19) 0.56 ≥40 1.49 (0.41 – 5.45) 0.54 0.24 (0.05 – 1.19) 0.08 0.66 (0.22 – 1.95) 0.45 Female gender 0.95 (0.38 – 2.37) 0.92 1.30 (0.44 – 3.83) 0.63 1.15 (0.52 – 2.52) 0.73 CD4 Result ≤100 REF REF REF 101-350 1.08 (0.39 – 2.96) 0.89 0.51 (0.13 – 1.96) 0.33 0.28 (0.11 – 0.75) 0.011 Read a complete sentence

2.14 (0.85 – 5.39) 0.11 4.54 (1.42 – 14.47) 0.011* 3.81 (1.61 – 9.03) 0.002*

Accessed sample SMS on enrollment†

3.05 (0.76 – 12.21) 0.12 0.63 (0.08 – 4.68) 0.65 4.90 (1.06 – 22.61)

0.04*

Randomized SMS Format Direct REF REF REF PIN 0.76 (0.27 – 2.17) 0.61 0.11 (0.03 – 0.44) 0.002* 0.26 (0.10 – 0.66) 0.005* Coded 1.00 (0.31 – 3.20) 0.99 0.38 (0.08 – 1.80) 0.22 0.58 (0.22 – 1.55) 0.28

Reported SMS Receipt Accurate SMS Identification

Return to Clinic <7 Days

AOR (95%CI) P-value AOR (95%CI) P-

value AOR (95%CI) P-value

Age <26 REF REF REF 26-32 0.97 (0.30 – 3.13) 0.97 0.33 (0.07 – 1.60) 0.17 0.71 (0.24 – 2.07) 0.53 33-39 1.43 (0.40 – 5.16) 0.59 0.98 (0.18 – 5.27) 0.99 0.72 (0.23 – 2.19) 0.56 ≥40 1.49 (0.41 – 5.45) 0.54 0.24 (0.05 – 1.19) 0.08 0.66 (0.22 – 1.95) 0.45 Female gender 0.95 (0.38 – 2.37) 0.92 1.30 (0.44 – 3.83) 0.63 1.15 (0.52 – 2.52) 0.73 CD4 Result ≤100 REF REF REF 101-350 1.08 (0.39 – 2.96) 0.89 0.51 (0.13 – 1.96) 0.33 0.28 (0.11 – 0.75) 0.011 Read a complete sentence

2.14 (0.85 – 5.39) 0.11 4.54 (1.42 – 14.47) 0.011* 3.81 (1.61 – 9.03) 0.002*

Accessed sample SMS on enrollment†

3.05 (0.76 – 12.21) 0.12 0.63 (0.08 – 4.68) 0.65 4.90 (1.06 – 22.61)

0.04*

Randomized SMS Format Direct REF REF REF PIN 0.76 (0.27 – 2.17) 0.61 0.11 (0.03 – 0.44) 0.002* 0.26 (0.10 – 0.66) 0.005* Coded 1.00 (0.31 – 3.20) 0.99 0.38 (0.08 – 1.80) 0.22 0.58 (0.22 – 1.55) 0.28

Reported SMS Receipt Accurate SMS Identification

Return to Clinic <7 Days

AOR (95%CI) P-value AOR (95%CI) P-

value AOR (95%CI) P-value

Age <26 REF REF REF 26-32 0.97 (0.30 – 3.13) 0.97 0.33 (0.07 – 1.60) 0.17 0.71 (0.24 – 2.07) 0.53 33-39 1.43 (0.40 – 5.16) 0.59 0.98 (0.18 – 5.27) 0.99 0.72 (0.23 – 2.19) 0.56 ≥40 1.49 (0.41 – 5.45) 0.54 0.24 (0.05 – 1.19) 0.08 0.66 (0.22 – 1.95) 0.45 Female gender 0.95 (0.38 – 2.37) 0.92 1.30 (0.44 – 3.83) 0.63 1.15 (0.52 – 2.52) 0.73 CD4 Result ≤100 REF REF REF 101-350 1.08 (0.39 – 2.96) 0.89 0.51 (0.13 – 1.96) 0.33 0.28 (0.11 – 0.75) 0.011 Read a complete sentence

2.14 (0.85 – 5.39) 0.11 4.54 (1.42 – 14.47) 0.011* 3.81 (1.61 – 9.03) 0.002*

Accessed sample SMS on enrollment†

3.05 (0.76 – 12.21) 0.12 0.63 (0.08 – 4.68) 0.65 4.90 (1.06 – 22.61) 0.04*

Randomized SMS Format Direct REF REF REF PIN 0.76 (0.27 – 2.17) 0.61 0.11 (0.03 – 0.44) 0.002* 0.26 (0.10 – 0.66) 0.005* Coded 1.00 (0.31 – 3.20) 0.99 0.38 (0.08 – 1.80) 0.22 0.58 (0.22 – 1.55) 0.28

†Restricted  to  par5cipants  with  an  available  cellular  phone  on  enrollment  

Reported SMS Receipt Accurate SMS Identification

Return to Clinic <7 Days

AOR (95%CI) P-value AOR (95%CI) P-

value AOR (95%CI) P-value

Age <26 REF REF REF 26-32 0.97 (0.30 – 3.13) 0.97 0.33 (0.07 – 1.60) 0.17 0.71 (0.24 – 2.07) 0.53 33-39 1.43 (0.40 – 5.16) 0.59 0.98 (0.18 – 5.27) 0.99 0.72 (0.23 – 2.19) 0.56 ≥40 1.49 (0.41 – 5.45) 0.54 0.24 (0.05 – 1.19) 0.08 0.66 (0.22 – 1.95) 0.45 Female gender 0.95 (0.38 – 2.37) 0.92 1.30 (0.44 – 3.83) 0.63 1.15 (0.52 – 2.52) 0.73 CD4 Result ≤100 REF REF REF 101-350 1.08 (0.39 – 2.96) 0.89 0.51 (0.13 – 1.96) 0.33 0.28 (0.11 – 0.75) 0.011 Read a complete sentence

2.14 (0.85 – 5.39) 0.11 4.54 (1.42 – 14.47) 0.011* 3.81 (1.61 – 9.03) 0.002*

Accessed sample SMS on enrollment†

3.05 (0.76 – 12.21) 0.12 0.63 (0.08 – 4.68) 0.65 4.90 (1.06 – 22.61)

0.04*

Randomized SMS Format Direct REF REF REF PIN 0.76 (0.27 – 2.17) 0.61 0.11 (0.03 – 0.44) 0.002* 0.26 (0.10 – 0.66) 0.005* Coded 1.00 (0.31 – 3.20) 0.99 0.38 (0.08 – 1.80) 0.22 0.58 (0.22 – 1.55) 0.28

Summary  1  

•  Strongest  predictor  of  receipt  and  response  to  SMS-­‐based  mHealth  intervenEon  was  proved  literacy  on  enrollment  

•  Cell  phone  literacy  also  appears  important  •  Gender,  age,  educaEonal  a1ainment  not  predicEve  

•  PIN-­‐protected  messages  are  challenging  •  Coded  messages  protect  privacy  without  challenging  feasibility  

Next  Steps  

•  Clinic  wide  automated  intervenEon  in  development  

•  Post-­‐intervenEon  effecEveness  •  Post-­‐intervenEon  acceptability  

Collaborators  and  Team  

•  MGH  CGH/HMS  •  Alexander  Lankowski  •  David  Bangsberg  •  Jessica  Haberer  •  Norma  Ware  •  Richard  Holden  (IU)  

•  MUST  •  Yap  Boum  •  Anthony  Wilson  •  Bosco  Bwana  •  Data  Santorino  •  Winnie  Muyindike  •  Isaac  Aturinda  •  Evans  Mwesigwa  

QuesEons  

•  Thank  you  for  your  a1enEon  

Mark  Siedner  msiedner@partners.org  

www.wirelesshealth2014.org